Review on Classification of Heart Disease using Hidden Pattern Analysis

Prediction of Heart Disease utilizing strategy of Data Mining is successful yet there is loss of Accuracy by utilizing the picture handling as extra preparing for more Accuracy . In Proposed System we are utilizing the calculations like Decision Tree, Nueral Network and the Naive Bayes in the information mining and in the picture Processing we are utilizing the prevalent calculations like Local Binary Pattern. The exploration result indicates forecast precision of 99 Percent. Information mining empower the wellbeing area to foresee designs in the datasets. Here we utilize picture preparing for looking at the ECG, CT examine, Angiography, and so forth, reports and finding the more exact outcomes

Towards new computational tools for predicting toxicity

The toxicological screening of the numerous chemicals that we are exposed to requires significant cost and the use of animals. Accordingly, more efficient methods for the evaluation of toxicity are required to reduce cost and the number of animals used. Computational strategies have the potential to reduce both the cost and the use of animal testing in toxicity screening. The ultimate goal of this thesis is to develop computational models for the prediction of toxicological endpoints that can serve as an alternative to animal testing. In Paper I, an attempt was made to construct a global quantitative structure-activity relationship (QSAR)model for the acute toxicity endpoint (LD50 values) using the Munro database that represents a broad chemical landscape. Such a model could be used for acute toxicity screening of chemicals of diverse structures. Paper II focuses on the use of acute toxicity data to support the prediction of chronic toxicity. The results of this study suggest that for related chemicals having acute toxicities within a similar range, their lowest observed effect levels (LOELs) can be used in read-across strategies to fill gaps in chronic toxicity data. In Paper III a k-nearest neighbor (k-NN) classification model was developed to predict human ether-a-go-go related gene (hERG)-derived toxicity. The results suggest that the model has potential for use in identifying compounds with hERG-liabilities, e.g. in drug development

Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy

A k-nearest neighbor (k-NN) classification model was constructed for 118 RDT NEDO (Repeated Dose Toxicity New Energy and industrial technology Development Organization; currently known as the Hazard Evaluation Support System (HESS)) database chemicals, employing two acute toxicity (LD50)-based classes as a response and using a series of eight PaDEL software-derived fingerprints as predictor variables. A model developed using Estate type fingerprints correctly predicted the LD50 classes for 70 of 94 training set chemicals and 19 of 24 test set chemicals. An individual category was formed for each of the chemicals by extracting its corresponding k-analogs that were identified by k-NN classification. These categories were used to perform the read-across study for prediction of the chronic toxicity, i.e., Lowest Observed Effect Levels (LOEL). We have successfully predicted the LOELs of 54 of 70 training set chemicals (77%) and 14 of 19 test set chemicals (74%) to within an order of magnitude from their experimental LOEL values. Given the success thus far, we conclude that if the k-NN model predicts LD50 classes correctly for a certain chemical, then the k-analogs of such a chemical can be successfully used for data gap filling for the LOEL. This model should support the in silico prediction of repeated dose toxicity

Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy

A k-nearest neighbor (k-NN) classification model was constructed for 118 RDT NEDO (Repeated Dose Toxicity New Energy and industrial technology Development Organization; currently known as the Hazard Evaluation Support System (HESS)) database chemicals, employing two acute toxicity (LD50)-based classes as a response and using a series of eight PaDEL software-derived fingerprints as predictor variables. A model developed using Estate type fingerprints correctly predicted the LD50 classes for 70 of 94 training set chemicals and 19 of 24 test set chemicals. An individual category was formed for each of the chemicals by extracting its corresponding k-analogs that were identified by k-NN classification. These categories were used to perform the read-across study for prediction of the chronic toxicity, i.e., Lowest Observed Effect Levels (LOEL). We have successfully predicted the LOELs of 54 of 70 training set chemicals (77%) and 14 of 19 test set chemicals (74%) to within an order of magnitude from their experimental LOEL values. Given the success thus far, we conclude that if the k-NN model predicts LD50classes correctly for a certain chemical, then the k-analogs of such a chemical can be successfully used for data gap filling for the LOEL. This model should support the in silico prediction of repeated dose toxicity

A k-nearest neighbor classification of hERG K+ channel blockers

A series of 172 molecular structures that block the hERG K+ channel were used to develop a classification model where, initially, eight types of PaDEL fingerprints were used for k-nearest neighbor model development. A consensus model constructed using Extended-CDK, PubChem and Substructure count fingerprint-based models was found to be a robust predictor of hERG activity. This consensus model demonstrated sensitivity and specificity values of 0.78 and 0.61 for the internal dataset compounds and 0.63 and 0.54 for the external (PubChem) dataset compounds, respectively. This model has identified the highest number of true positives (i.e. 140) from the PubChem dataset so far, as compared to other published models, and can potentially serve as a basis for the prediction of hERG active compounds. Validating this model against FDA-withdrawn substances indicated that it may even be useful for differentiating between mechanisms underlying QT prolongation

Synthesis of Thienopyrimidines and their Antipsychotic Activity

A series of thienopyrimidines and related heterocycles were synthesized by refluxing related imidoyl chloride with primary and secondary amines under microwave irradiation and classical heating. The imidoyl chlorides were synthesized from corresponding cyclic imides with phosphorus oxychlorides under microwave irradiation and classical heating. The structures of the compounds were confirmed by FT-IR, NMR. The synthesized compounds were screened for anti psychotic activity

Analysis of apical third root canal morphology of the palatal root of maxillary first molar and its proximity to maxillary sinus: A cone-beam computed tomographic study

Aims: The aim of this study was to assess the angulation of the apical exit from radiographic apex of palatal root of maxillary first molar, to measure the distance between radiographic apex and apical exit of palatal root of maxillary first molar and to measure the distance of apical exit of palatal root of maxillary first molar from maxillary sinus floor. Materials and Methods: A total of 118 untreated, well-developed maxillary first molars were selected on cone-beam computed tomography scans. Data were collected and viewed by invivo5 software. Descriptive statistical analysis was given as mean value. Results: Radiographic apex and apical exit did not coincide in the large number of samples. The palatal root of maxillary first molar was found to be in direct contact with the floor of maxillary sinus in maximum samples. Conclusion: Apical exit does not coincide with the radiographic apex in all the cases. The distance between radiographic apex and apical foramina or apical exit ranges from 0 to 1.43 mm. The apical exit or apical foramina are in direct contact with maxillary sinus floor in 75% cases

Towards Global QSAR Model Building for Acute Toxicity : Munro Database Case Study

A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity

Combining In Vivo, In Vitro And Toxicokinetics Data In Read-across: A Case Study Using Caffeine

Presented at the Annual Society of Toxicology meetin